Recently, there has been an explosive growth of digital imaging data, resulting in challenge and opportunity. Contemporary computational techniques can analyze immense amounts of complex data and are being incorporated into decision-support applications.
The annual $100 B medical imaging business is currently based on a high tech, ‘picture taking’ front end that feeds a low tech human back end that is responsible for creating the official end product—the Report. In this traditional process the extensive and remarkably sophisticated information contained in today's computer generated medical images from CT, MR, PET and real-time ultrasound systems ends up inadequately encapsulated in a radiologist's brief, subjective, qualitative report of less than a 100 words. The technological mismatch between contemporary digital imaging devices and analog human readers is not only operationally inefficient but also greatly limits the clinical value of the studies.
Medical image interpretation techniques have not kept up with medical image generation technology. Imaging in general and biomedical imaging in particular has two distinct elements—image production and image interpretation. Both are necessary for any practical application. While intimately related and mutually necessary, image production and interpretation are actually separate processes. Over the last 30 years there has been an ‘explosion’ of new and increasingly useful medical image production technology with CT, MRI, US, PET and SPECT now joining x-rays in exquisitely incorporating signals from most body parts into visually compelling images. Unfortunately, image interpretation, has not changed since Roentgen's time. The medical image interpreter is still the traditional human observer, albeit more highly trained and knowledgeable than the observer of 1900. While specialized human observers such as radiologists perform their interpretative tasks remarkably well, they by necessity use and are fundamentally limited by the human visual system. In addition, current radiology workflow is inefficient. The average time for a radiologist to view and report a standard radiographic study is 3 minutes, of which approximately 25% is dedicated to the mechanics of report generation. The administrative components necessary for report generation are not merely tedious, but are distinctly unpleasant, made even more so by current voice recognition (VR) systems.
All radiologists and other imaging specialists are intrinsically variable in their observations, insensitive to small signal changes, unappreciative of complex spatial patterns and are non-quantitative. As image acquisition technology improves, these human limitations increasingly constrain the useful information available from more refined image data. Medical images are now intrinsically digital, all being derived from computer processing. In contrast to human image analysis, computational results are invariable, sensitive to subtle signal changes imperceptible to the naked human eye (fMRI), reflective of very complex spatial patterns (Alzheimer Disease atrophy patterns) and intrinsically quantitative, allowing much more sophisticated statistical analysis. Computer analysis also offers the opportunity to reduce the costs of medical image analysis, having computers more economically and efficiently perform mundane interpretative tasks, leaving more demanding tasks for the more expensive specialized reader. However, while there are numerous biomedical image analysis computer algorithms, most work at a relatively leisurely pace, and have limited scope, operating in an ad hoc fashion on very narrowly defined tasks proscribed by specific research hypothesis.
Not perceiving an image finding is a major contributor to missed diagnosis, the most important clinical and medicolegal interpretative error. A long-recognized mechanism for decreasing missed findings is ‘double reading.’ Double reading involves two human observers viewing and interpreting the same image study. This method has been shown to decrease missed diagnosis. Unfortunately, the logistical demands and cost of double reading has prohibited its general use, except in some specific circumstances. Double reading of screening mammograms was the standard of care in Great Britain. This practice has now been updated by using mammographic CAD software to perform the ‘second read.’ In terms of sensitivity and specificity, the combination of a radiologist and CAD has been demonstrated in a large clinical trial to perform as well as two radiologists, but with quicker turn-around time and less expense. While this study demonstrates the concept of using computer image analysis to improve both the interpretative quality and efficiency of a single radiologist, the practice has failed to be adopted widely in radiologic practice.
There are limitation(s) in current software capabilities/approaches that prevent their broad use for medical image interpretation. Though the concept of obtaining diagnostic support from computer software is not new, early systems had limited clinical success due to immature software capabilities, hardware limitations, operational inefficiencies, and cost. While improvements in technology have enabled utilization in such fields as the interpretation of mammograms, screening mammography is a relatively unique interpretive task with very limited diagnostic choice and little urgency. These conditions can be met with ‘niche’ software and relatively primitive computational resources. To date there are no image analysis software tools that are robust enough to be applied broadly to medical image interpretation and able to operate within the acute care timeframe. Modern image analysis software remains very task specific (though SPM and various image analysis toolkits offer broader analytical capabilities), operationally demanding, and slow relative to the clinical environment.
There is a need for a systematic method and system of interpreting medical images that is efficient, inexpensive and minimizes human error.